In characterizing hydrocarbon reservoirs, estimating reserves, and developing models for how to best extract the hydrocarbons, it is useful to know the lithology (for example, relative amounts of shale and sand) and associated porosity of the rocks in the target interval. Rock properties can be measured directly from rock samples obtained from wells but such samples are generally very limited in availability due to the expense of drilling those wells. These properties can also be inferred from seismic data. Because of the complicated nature of the theoretical relationships between the seismic data (reflectivity) and the important rock properties (lithology, porosity, and fluid content), these two quantities are often related in practice through empirical relationships derived at wells, where both seismic and well measurements coexist. These empirical relationships are then applied to the entire volume of seismic data (or attributes derived from them) in order to make predictions about rock properties away from the wells. The problem is that empirical models require a statistically significant sampling of data and yet the wells provide very limited and generally biased samples of the reservoir properties. In regions where a large number of wells have been drilled, pattern-based recognition methods and simple empirical relationships can be used successfully to infer rock properties from seismic data. However, in regions of limited well control, it is difficult to make accurate lithology predictions using empirical relationships derived from just a few wells.
A commonly used method for determining clay content and porosity from seismic data (or attributes of the seismic data) is to use linear regression to solve an equation of the following form:Impedance=A·φ+B·vshale+Cwhere φ is the porosity, vshale is the shale volume and A, B and C are the constants that relate the porosity, vshale and impedances (or some other seismic attribute of interest) to one another. Regression methods are more robust when they are used with larger datasets obtained from wells penetrating different sections of the reservoir so that there is a statistically significant sampling of the data. In regions of limited well control the relationships derived in this manner cannot be used with confidence.
Another class of methods used to predict clay content and porosity from seismic data uses pattern recognition, often implemented with neural networks, to construct the necessary relationships. These methods use a training set to identify patterns between the well and the seismic data and then classify the remainder of the seismic data set according to the patterns observed in the training set. The resulting relationships can be quite complicated (and certainly allow more complexity than the simple linear regression of the above-stated equation), but they are still fundamentally empirical relationships based on observations at the well rather than on a physical description. Consequently, these methods suffer from the same problem as the regression methods in that they require enough data examples (wells) in order to train the network competently. With sufficient well control they can be very good interpolators (although generally poor extrapolators). In regions of limited of well control, they are unreliable interpolators (as well as poor extrapolators).